README.md

metadata

license: mit
task_categories:
  - text-generation
language:
  - en
tags:
  - sft
  - conversational
  - instruction-tuning
  - multi-genre
  - math-reasoning
  - humour
  - genz
  - agent
size_categories:
  - 10K-100K
configs:
  - config_name: math_reasoning
    data_files: math_reasoning/*.parquet
  - config_name: humour_chat
    data_files: humour_chat/*.parquet
  - config_name: merged_genz_chat
    data_files: merged_genz_chat/*.parquet
  - config_name: agent_chat
    data_files: agent_chat/*.parquet

Filthy-data-SFT

This is a highly curated, cleaned, and structurally normalized version of the Arko007/Filthy-data dataset. Every file across all genres has been meticulously mapped into a standard SFT conversational sequence.

Strict Data Quality Filtering

To protect models during fine-tuning from learning corrupt or blank behaviors, we applied a strict Data Quality Pipeline:

• No Empty Turns: Any prompt/response containing empty text strings ("") was thoroughly stripped out.
• Coherent Conversations: Removed conversational turns with null or invalid roles.
• Complete Conversational Loops: Dropped any thread that didn't have at least one valid user message and assistant answer.

Subsets & Genre Overview

All records in this repository are saved as high-performance Parquet files organized into subdirectory paths corresponding directly to their genres.

Genre Subset	Cleaned Records	Description
math_reasoning	20504	Curated mathematical problems, reasoning lines, and step-by-step logic
humour_chat	5017	Funny, witty, and contextual dialogue streams
merged_genz_chat	1190	Unified and restructured slang/colloquial GenZ and extreme filthy conversations
agent_chat	22333	System actions, structured rules, and agentic workflows

---

Data Schema

Every split matches this uniform, nested conversational schema:

• messages (list of dicts):
  • role (string): Either "user" or "assistant".
  • content (string): Dialogue payload.

Sample Representation

{
  "messages": [
    {
      "role": "user",
      "content": "Yo, what is the vibe today?"
    },
    {
      "role": "assistant",
      "content": "No cap, we are just cooling out and vibing!"
    }
  ]
}

---

Quick Start

from datasets import load_dataset

# Load specific subsets seamlessly
agent_dataset = load_dataset("Arko007/Filthy-data-SFT", "agent_chat")
genz_dataset = load_dataset("Arko007/Filthy-data-SFT", "merged_genz_chat")

print(genz_dataset["train"][0])